Estimating the productive potential of five natural forest types in northeastern China
Abstract Background There is a serious lack of experience regarding the productive potential of the natural forests in northeastern China, which severely limits the development of sustainable forest management strategies for this most important forest region in China. Accordingly, the objective of t...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2019-10-01
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| Series: | Forest Ecosystems |
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| Online Access: | http://link.springer.com/article/10.1186/s40663-019-0204-0 |
| _version_ | 1797968371524829184 |
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| author | Zhaofei Wu Zhonghui Zhang Juan Wang |
| author_facet | Zhaofei Wu Zhonghui Zhang Juan Wang |
| author_sort | Zhaofei Wu |
| collection | DOAJ |
| description | Abstract Background There is a serious lack of experience regarding the productive potential of the natural forests in northeastern China, which severely limits the development of sustainable forest management strategies for this most important forest region in China. Accordingly, the objective of this study is to develop a first comprehensive system for estimating the wood production for the five dominant forest types. Methods Based on a network of 384 field plots and using the state-space approach, we develop a system of dynamic stand models, for each of the five main forest types. Four models were developed and evaluated, including a base model and three extended models which include the effects of dominant height and climate variables. The four models were fitted, and their predictive strengths were tested, using the “seemingly unrelated regression” (SUR) technique. Results All three of the extended models increased the accuracy of the predictions at varying degrees for the five major natural forest types of northeastern China. The inclusion of dominant height and two climate factors (precipitation and temperature) in the base model resulted in the best performance for all the forest types. On average, the root mean square values were reduced by 13.0% when compared with the base model. Conclusion Both dominant height and climate factors were important variables in estimating forest production. This study not only presents a new method for estimating forest production for a large region, but also explains regional differences in the effect of site productivity and climate. |
| first_indexed | 2024-04-11T02:44:46Z |
| format | Article |
| id | doaj.art-c51cee86fcea4a079e2587ef84967feb |
| institution | Directory Open Access Journal |
| issn | 2197-5620 |
| language | English |
| last_indexed | 2024-04-11T02:44:46Z |
| publishDate | 2019-10-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Forest Ecosystems |
| spelling | doaj.art-c51cee86fcea4a079e2587ef84967feb2023-01-02T18:08:52ZengKeAi Communications Co., Ltd.Forest Ecosystems2197-56202019-10-016111110.1186/s40663-019-0204-0Estimating the productive potential of five natural forest types in northeastern ChinaZhaofei Wu0Zhonghui Zhang1Juan Wang2Research Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry UniversityJilin Provincial Academy of Forestry SciencesResearch Center of Forest Management Engineering of State Forestry and Grassland Administration, Beijing Forestry UniversityAbstract Background There is a serious lack of experience regarding the productive potential of the natural forests in northeastern China, which severely limits the development of sustainable forest management strategies for this most important forest region in China. Accordingly, the objective of this study is to develop a first comprehensive system for estimating the wood production for the five dominant forest types. Methods Based on a network of 384 field plots and using the state-space approach, we develop a system of dynamic stand models, for each of the five main forest types. Four models were developed and evaluated, including a base model and three extended models which include the effects of dominant height and climate variables. The four models were fitted, and their predictive strengths were tested, using the “seemingly unrelated regression” (SUR) technique. Results All three of the extended models increased the accuracy of the predictions at varying degrees for the five major natural forest types of northeastern China. The inclusion of dominant height and two climate factors (precipitation and temperature) in the base model resulted in the best performance for all the forest types. On average, the root mean square values were reduced by 13.0% when compared with the base model. Conclusion Both dominant height and climate factors were important variables in estimating forest production. This study not only presents a new method for estimating forest production for a large region, but also explains regional differences in the effect of site productivity and climate.http://link.springer.com/article/10.1186/s40663-019-0204-0Forest typesForest growthClimateSite conditionsSeemingly unrelated regression |
| spellingShingle | Zhaofei Wu Zhonghui Zhang Juan Wang Estimating the productive potential of five natural forest types in northeastern China Forest Ecosystems Forest types Forest growth Climate Site conditions Seemingly unrelated regression |
| title | Estimating the productive potential of five natural forest types in northeastern China |
| title_full | Estimating the productive potential of five natural forest types in northeastern China |
| title_fullStr | Estimating the productive potential of five natural forest types in northeastern China |
| title_full_unstemmed | Estimating the productive potential of five natural forest types in northeastern China |
| title_short | Estimating the productive potential of five natural forest types in northeastern China |
| title_sort | estimating the productive potential of five natural forest types in northeastern china |
| topic | Forest types Forest growth Climate Site conditions Seemingly unrelated regression |
| url | http://link.springer.com/article/10.1186/s40663-019-0204-0 |
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